The amount of data in the world has been increasing over time, and analyzing large data sets (i.e., big data) has increasingly become a basis of competition, supporting productivity growth, innovation, and consumer surplus, according to recent research statistics. For example, different market sectors such as healthcare, retail, manufacturing and personal-location data generate enormous amounts of data. As organizations create, store and analyze more data, performance can improve on everything from product inventories to employee productivity and ultimately into their bottom line. Intelligent data collection and advanced analysis can facilitate better management decisions and forecasting. However, big data can be so voluminous and unstructured that organizing it for meaningful analysis is complex and time consuming, not to mention that amount of storage space that is required to maintain the data. Moreover, analyzing big data can suffer from various problems, including missing relevant data, inaccurate algorithms, incorrect assumptions, etc. Thus, many challenges exists in the collection and processing of big data. Such challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. Determining how to organize and analyze big data to meet these challenges is a time consuming and daunting task.